moved scripts to /ind_scripts & added add col to formatting script
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6 changed files with 1129 additions and 62 deletions
340
mcsm/ind_scripts/format_results.py
Executable file
340
mcsm/ind_scripts/format_results.py
Executable file
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#!/usr/bin/env python3
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#=======================================================================
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#TASK:
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#=======================================================================
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#%% load packages
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import os,sys
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import subprocess
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import argparse
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#import requests
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import re
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#import time
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import pandas as pd
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from pandas.api.types import is_string_dtype
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from pandas.api.types import is_numeric_dtype
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import numpy as np
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from mcsm import *
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#=======================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/mcsm')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output
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#drug = 'pyrazinamide'
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#gene = 'pncA'
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drug = 'isoniazid'
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gene = 'KatG'
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#drug = args.drug
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#gene = args.gene
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gene_match = gene + '_p.'
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#==========
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# data dir
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#==========
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datadir = homedir + '/' + 'git/Data'
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#=======
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# input:
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#=======
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# 1) result_urls (from outdir)
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outdir = datadir + '/' + drug + '/' + 'output'
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in_filename = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py)
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infile = outdir + '/' + in_filename
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print('Input filename:', in_filename
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, '\nInput path(from output dir):', outdir
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, '\n=============================================================')
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#=======
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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out_filename = gene.lower() + '_complex_mcsm_results.csv'
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outfile = outdir + '/' + out_filename
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print('Output filename:', out_filename
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, '\nOutput path:', outdir
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, '\n=============================================================')
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#%%=====================================================================
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def format_mcsm_output(mcsm_outputcsv):
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"""
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@param mcsm_outputcsv: file containing mcsm results for all muts
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which is the result of build_result_dict() being called for each
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mutation and then converting to a pandas df and output as csv.
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@type string
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@return formatted mcsm output
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@type pandas df
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"""
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#############
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# Read file
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#############
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mcsm_data = pd.read_csv(mcsm_outputcsv, sep = ',')
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dforig_shape = mcsm_data.shape
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print('dimensions of input file:', dforig_shape)
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#############
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# rename cols
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#############
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# format colnames: all lowercase, remove spaces and use '_' to join
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print('Assigning meaningful colnames i.e without spaces and hyphen and reflecting units'
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, '\n===================================================================')
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my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog' # relevant info from this col will be extracted and the column discarded
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, 'Mutation information': 'mutation_information' # {wild_type}<position>{mutant_type}
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, 'Wild-type': 'wild_type' # one letter amino acid code
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, 'Position': 'position' # number
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, 'Mutant-type': 'mutant_type' # one letter amino acid code
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, 'Chain': 'chain' # single letter (caps)
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, 'Ligand ID': 'ligand_id' # 3-letter code
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, 'Distance to ligand': 'ligand_distance' # angstroms
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, 'DUET stability change': 'duet_stability_change'} # in kcal/mol
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mcsm_data.rename(columns = my_colnames_dict, inplace = True)
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#%%===========================================================================
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#################################
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# populate mutation_information
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# col which is currently blank
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#################################
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# populate mutation_information column:mcsm style muts {WT}<POS>{MUT}
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print('Populating column : mutation_information which is currently empty\n', mcsm_data['mutation_information'])
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mcsm_data['mutation_information'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str) + mcsm_data['mutant_type']
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print('checking after populating:\n', mcsm_data['mutation_information']
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, '\n===================================================================')
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# Remove spaces b/w pasted columns
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print('removing white space within column: \mutation_information')
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mcsm_data['mutation_information'] = mcsm_data['mutation_information'].str.replace(' ', '')
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print('Correctly formatted column: mutation_information\n', mcsm_data['mutation_information']
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, '\n===================================================================')
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#%%===========================================================================
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#############
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# sanity check: drop dupliate muts
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#############
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# shouldn't exist as this should be eliminated at the time of running mcsm
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print('Sanity check:'
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, '\nChecking duplicate mutations')
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if mcsm_data['mutation_information'].duplicated().sum() == 0:
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print('PASS: No duplicate mutations detected (as expected)'
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, '\nDim of data:', mcsm_data.shape
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, '\n===============================================================')
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else:
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print('FAIL (but not fatal): Duplicate mutations detected'
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, '\nDim of df with duplicates:', mcsm_data.shape
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, 'Removing duplicate entries')
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mcsm_data = mcsm_data.drop_duplicates(['mutation_information'])
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print('Dim of data after removing duplicate muts:', mcsm_data.shape
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, '\n===============================================================')
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#%%===========================================================================
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#############
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# Create col: duet_outcome
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#############
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# classification based on DUET stability values
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print('Assigning col: duet_outcome based on DUET stability values')
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print('Sanity check:')
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# count positive values in the DUET column
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c = mcsm_data[mcsm_data['duet_stability_change']>=0].count()
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DUET_pos = c.get(key = 'duet_stability_change')
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# Assign category based on sign (+ve : Stabilising, -ve: Destabilising, Mind the spelling (British spelling))
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mcsm_data['duet_outcome'] = np.where(mcsm_data['duet_stability_change']>=0, 'Stabilising', 'Destabilising')
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mcsm_data['duet_outcome'].value_counts()
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if DUET_pos == mcsm_data['duet_outcome'].value_counts()['Stabilising']:
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print('PASS: DUET outcome assigned correctly')
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else:
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print('FAIL: DUET outcome assigned incorrectly'
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, '\nExpected no. of stabilising mutations:', DUET_pos
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, '\nGot no. of stabilising mutations', mcsm_data['duet_outcome'].value_counts()['Stabilising']
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, '\n===============================================================')
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#%%===========================================================================
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#############
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# Extract numeric
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# part of ligand_distance col
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#############
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# Extract only the numeric part from col: ligand_distance
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# number: '-?\d+\.?\d*'
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mcsm_data['ligand_distance']
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print('extracting numeric part of col: ligand_distance')
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mcsm_data['ligand_distance'] = mcsm_data['ligand_distance'].str.extract('(\d+\.?\d*)')
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mcsm_data['ligand_distance']
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#%%===========================================================================
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#############
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# Create 2 columns:
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# ligand_affinity_change and ligand_outcome
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#############
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# the numerical and categorical parts need to be extracted from column: PredAffLog
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# regex used
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# numerical part: '-?\d+\.?\d*'
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# categorocal part: '\b(\w+ing)\b'
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print('Extracting numerical and categorical parts from the col: PredAffLog')
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print('to create two columns: ligand_affinity_change and ligand_outcome'
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, '\n===================================================================')
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# 1) Extracting the predicted affinity change (numerical part)
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mcsm_data['ligand_affinity_change'] = mcsm_data['PredAffLog'].str.extract('(-?\d+\.?\d*)', expand = True)
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print(mcsm_data['ligand_affinity_change'])
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# 2) Extracting the categorical part (Destabillizing and Stabilizing) using word boundary ('ing')
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#aff_regex = re.compile(r'\b(\w+ing)\b')
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mcsm_data['ligand_outcome']= mcsm_data['PredAffLog'].str.extract(r'(\b\w+ing\b)', expand = True)
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print(mcsm_data['ligand_outcome'])
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print(mcsm_data['ligand_outcome'].value_counts())
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#############
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# changing spelling: British
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#############
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# ensuring spellings are consistent
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american_spl = mcsm_data['ligand_outcome'].value_counts()
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print('Changing to Bristish spellings for col: ligand_outcome')
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mcsm_data['ligand_outcome'].replace({'Destabilizing': 'Destabilising', 'Stabilizing': 'Stabilising'}, inplace = True)
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print(mcsm_data['ligand_outcome'].value_counts())
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british_spl = mcsm_data['ligand_outcome'].value_counts()
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# compare series values since index will differ from spelling change
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check = american_spl.values == british_spl.values
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if check.all():
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print('PASS: spelling change successfull'
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, '\nNo. of predicted affinity changes:\n', british_spl
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, '\n===============================================================')
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else:
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print('FAIL: spelling change unsucessfull'
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, '\nExpected:\n', american_spl
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, '\nGot:\n', british_spl
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, '\n===============================================================')
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#%%===========================================================================
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#############
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# ensuring corrrect dtype columns
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#############
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# check dtype in cols
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print('Checking dtypes in all columns:\n', mcsm_data.dtypes
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, '\n===================================================================')
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print('Converting the following cols to numeric:'
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, '\nligand_distance'
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, '\nduet_stability_change'
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, '\nligand_affinity_change'
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, '\n===================================================================')
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# using apply method to change stabilty and affinity values to numeric
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numeric_cols = ['duet_stability_change', 'ligand_affinity_change', 'ligand_distance']
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mcsm_data[numeric_cols] = mcsm_data[numeric_cols].apply(pd.to_numeric)
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# check dtype in cols
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print('checking dtype after conversion')
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cols_check = mcsm_data.select_dtypes(include='float64').columns.isin(numeric_cols)
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if cols_check.all():
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print('PASS: dtypes for selected cols:', numeric_cols
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, '\nchanged to numeric'
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, '\n===============================================================')
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else:
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print('FAIL:dtype change to numeric for selected cols unsuccessful'
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, '\n===============================================================')
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print(mcsm_data.dtypes)
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#%%===========================================================================
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#############
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# scale duet values
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#############
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# Rescale values in DUET_change col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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duet_min = mcsm_data['duet_stability_change'].min()
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duet_max = mcsm_data['duet_stability_change'].max()
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duet_scale = lambda x : x/abs(duet_min) if x < 0 else (x/duet_max if x >= 0 else 'failed')
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mcsm_data['duet_scaled'] = mcsm_data['duet_stability_change'].apply(duet_scale)
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print('Raw duet scores:\n', mcsm_data['duet_stability_change']
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, '\n---------------------------------------------------------------'
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, '\nScaled duet scores:\n', mcsm_data['duet_scaled'])
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#%%===========================================================================
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#############
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# scale affinity values
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#############
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# rescale values in affinity change col b/w -1 and 1 so negative numbers
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# stay neg and pos numbers stay positive
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aff_min = mcsm_data['ligand_affinity_change'].min()
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aff_max = mcsm_data['ligand_affinity_change'].max()
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aff_scale = lambda x : x/abs(aff_min) if x < 0 else (x/aff_max if x >= 0 else 'failed')
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mcsm_data['affinity_scaled'] = mcsm_data['ligand_affinity_change'].apply(aff_scale)
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print('Raw affinity scores:\n', mcsm_data['ligand_affinity_change']
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, '\n---------------------------------------------------------------'
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, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
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#=============================================================================
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# Adding colname: wild_pos: sometimes useful for plotting and db
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print('Creating column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
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print(mcsm_data['wild_position'].head())
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# Remove spaces b/w pasted columns
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print('removing white space within column: wild_position')
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mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '')
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print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head()
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, '\n===================================================================')
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#=============================================================================
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#%% ensuring dtypes are string for the non-numeric cols
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#) char cols
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char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain'
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, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position']
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#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
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cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols)
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if cols_check_char.all():
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print('PASS: dtypes for char cols:', char_cols, 'are indeed string'
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, '\n===============================================================')
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else:
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print('FAIL:dtype change to numeric for selected cols unsuccessful'
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, '\n===============================================================')
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#mcsm_data['ligand_distance', 'ligand_affinity_change'].apply(is_numeric_dtype(mcsm_data['ligand_distance', 'ligand_affinity_change']))
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print(mcsm_data.dtypes)
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#=============================================================================
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# Removing PredAff log column as it is not needed?
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print('Removing col: PredAffLog since relevant info has been extracted from it')
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mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
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#%%===========================================================================
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#############
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# sanity check before writing file
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#############
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expected_ncols_toadd = 5 # beware of hardcoded numbers
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dforig_len = dforig_shape[1]
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expected_cols = dforig_len + expected_ncols_toadd
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if len(mcsm_dataf.columns) == expected_cols:
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print('PASS: formatting successful'
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, '\nformatted df has expected no. of cols:', expected_cols
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, '\ncolnames:', mcsm_dataf.columns
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, '\n----------------------------------------------------------------'
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, '\ndtypes in cols:', mcsm_dataf.dtypes
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, '\n----------------------------------------------------------------'
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, '\norig data shape:', dforig_shape
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, '\nformatted df shape:', mcsm_dataf.shape
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, '\n===============================================================')
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else:
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print('FAIL: something went wrong in formatting df'
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, '\nLen of orig df:', dforig_len
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, '\nExpected number of cols to add:', expected_ncols_toadd
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, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
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, '\nGot no. of cols:', len(mcsm_dataf.columns)
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, '\nCheck formatting:'
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, '\ncheck hardcoded value:', expected_ncols_toadd
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, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
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, '\n===============================================================')
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return mcsm_dataf
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#=======================================================================
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# call function
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mcsm_df_formatted = format_mcsm_output(infile)
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# writing file
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print('Writing formatted df to csv')
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mcsm_df_formatted.to_csv(outfile, index = False)
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print('Finished writing file:'
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, '\nFile', outfile
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, '\nExpected no. of rows:', len(mcsm_df_formatted)
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, '\nExpected no. of cols:', len(mcsm_df_formatted)
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, '\n=============================================================')
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#%%
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#End of script
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299
mcsm/ind_scripts/format_results_notdef.py
Executable file
299
mcsm/ind_scripts/format_results_notdef.py
Executable file
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@ -0,0 +1,299 @@
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#!/usr/bin/env python3
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#=======================================================================
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#TASK:
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#=======================================================================
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#%% load packages
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import os,sys
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import subprocess
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import argparse
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#import requests
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import re
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#import time
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import pandas as pd
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from pandas.api.types import is_string_dtype
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from pandas.api.types import is_numeric_dtype
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import numpy as np
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#=======================================================================
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#%% specify input and curr dir
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homedir = os.path.expanduser('~')
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# set working dir
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os.getcwd()
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os.chdir(homedir + '/git/LSHTM_analysis/mcsm')
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os.getcwd()
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#=======================================================================
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#%% variable assignment: input and output
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#drug = 'pyrazinamide'
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#gene = 'pncA'
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drug = 'rifampicin'
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gene = 'rpoB'
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#drug = args.drug
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#gene = args.gene
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gene_match = gene + '_p.'
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#==========
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# data dir
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#==========
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datadir = homedir + '/' + 'git/Data'
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#=======
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# input:
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#=======
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# 1) result_urls (from outdir)
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outdir = datadir + '/' + drug + '/' + 'output'
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in_filename = gene.lower() + '_mcsm_output.csv' #(outfile, from mcsm_results.py)
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infile = outdir + '/' + in_filename
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print('Input filename:', in_filename
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, '\nInput path(from output dir):', outdir
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, '\n=============================================================')
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#=======
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# output
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#=======
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outdir = datadir + '/' + drug + '/' + 'output'
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out_filename = gene.lower() + '_complex_mcsm_norm.csv'
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outfile = outdir + '/' + out_filename
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print('Output filename:', out_filename
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, '\nOutput path:', outdir
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, '\n=============================================================')
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#=======================================================================
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print('Reading input file')
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mcsm_data = pd.read_csv(infile, sep = ',')
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mcsm_data.columns
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# PredAffLog = affinity_change_log
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# "DUETStability_Kcalpermol = DUET_change_kcalpermol
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dforig_shape = mcsm_data.shape
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print('dim of infile:', dforig_shape)
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#############
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# rename cols
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#############
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# format colnames: all lowercase, remove spaces and use '_' to join
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print('Assigning meaningful colnames i.e without spaces and hyphen and reflecting units'
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, '\n===================================================================')
|
||||
my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog' # relevant info from this col will be extracted and the column discarded
|
||||
, 'Mutation information': 'mutation_information' # {wild_type}<position>{mutant_type}
|
||||
, 'Wild-type': 'wild_type' # one letter amino acid code
|
||||
, 'Position': 'position' # number
|
||||
, 'Mutant-type': 'mutant_type' # one letter amino acid code
|
||||
, 'Chain': 'chain' # single letter (caps)
|
||||
, 'Ligand ID': 'ligand_id' # 3-letter code
|
||||
, 'Distance to ligand': 'ligand_distance' # angstroms
|
||||
, 'DUET stability change': 'duet_stability_change'} # in kcal/mol
|
||||
|
||||
mcsm_data.rename(columns = my_colnames_dict, inplace = True)
|
||||
#%%===========================================================================
|
||||
# populate mutation_information column:mcsm style muts {WT}<POS>{MUT}
|
||||
print('Populating column : mutation_information which is currently empty\n', mcsm_data['mutation_information'])
|
||||
mcsm_data['mutation_information'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str) + mcsm_data['mutant_type']
|
||||
print('checking after populating:\n', mcsm_data['mutation_information']
|
||||
, '\n===================================================================')
|
||||
|
||||
# Remove spaces b/w pasted columns
|
||||
print('removing white space within column: \mutation_information')
|
||||
mcsm_data['mutation_information'] = mcsm_data['mutation_information'].str.replace(' ', '')
|
||||
print('Correctly formatted column: mutation_information\n', mcsm_data['mutation_information']
|
||||
, '\n===================================================================')
|
||||
#%% Remove whitespace from column
|
||||
#orig_dtypes = mcsm_data.dtypes
|
||||
#https://stackoverflow.com/questions/33788913/pythonic-efficient-way-to-strip-whitespace-from-every-pandas-data-frame-cell-tha/33789292
|
||||
#mcsm_data.columns = mcsm_data.columns.str.strip()
|
||||
#new_dtypes = mcsm_data.dtypes
|
||||
#%%===========================================================================
|
||||
# very important
|
||||
print('Sanity check:'
|
||||
, '\nChecking duplicate mutations')
|
||||
if mcsm_data['mutation_information'].duplicated().sum() == 0:
|
||||
print('PASS: No duplicate mutations detected (as expected)'
|
||||
, '\nDim of data:', mcsm_data.shape
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL (but not fatal): Duplicate mutations detected'
|
||||
, '\nDim of df with duplicates:', mcsm_data.shape
|
||||
, 'Removing duplicate entries')
|
||||
mcsm_data = mcsm_data.drop_duplicates(['mutation_information'])
|
||||
print('Dim of data after removing duplicate muts:', mcsm_data.shape
|
||||
, '\n===============================================================')
|
||||
#%%===========================================================================
|
||||
# create duet_outcome column: classification based on DUET stability values
|
||||
print('Assigning col: duet_outcome based on DUET stability values')
|
||||
print('Sanity check:')
|
||||
# count positive values in the DUET column
|
||||
c = mcsm_data[mcsm_data['duet_stability_change']>=0].count()
|
||||
DUET_pos = c.get(key = 'duet_stability_change')
|
||||
# Assign category based on sign (+ve : Stabilising, -ve: Destabilising, Mind the spelling (British spelling))
|
||||
mcsm_data['duet_outcome'] = np.where(mcsm_data['duet_stability_change']>=0, 'Stabilising', 'Destabilising')
|
||||
mcsm_data['duet_outcome'].value_counts()
|
||||
if DUET_pos == mcsm_data['duet_outcome'].value_counts()['Stabilising']:
|
||||
print('PASS: DUET outcome assigned correctly')
|
||||
else:
|
||||
print('FAIL: DUET outcome assigned incorrectly'
|
||||
, '\nExpected no. of stabilising mutations:', DUET_pos
|
||||
, '\nGot no. of stabilising mutations', mcsm_data['duet_outcome'].value_counts()['Stabilising']
|
||||
, '\n===============================================================')
|
||||
#%%===========================================================================
|
||||
# Extract only the numeric part from col: ligand_distance
|
||||
# number: '-?\d+\.?\d*'
|
||||
mcsm_data['ligand_distance']
|
||||
print('extracting numeric part of col: ligand_distance')
|
||||
mcsm_data['ligand_distance'] = mcsm_data['ligand_distance'].str.extract('(\d+\.?\d*)')
|
||||
mcsm_data['ligand_distance']
|
||||
#%%===========================================================================
|
||||
# create ligand_outcome column: classification based on affinity change values
|
||||
# the numerical and categorical parts need to be extracted from column: PredAffLog
|
||||
# regex used
|
||||
# number: '-?\d+\.?\d*'
|
||||
# category: '\b(\w+ing)\b'
|
||||
print('Extracting numerical and categorical parts from the col: PredAffLog')
|
||||
print('to create two columns: ligand_affinity_change and ligand_outcome'
|
||||
, '\n===================================================================')
|
||||
# Extracting the predicted affinity change (numerical part)
|
||||
mcsm_data['ligand_affinity_change'] = mcsm_data['PredAffLog'].str.extract('(-?\d+\.?\d*)', expand = True)
|
||||
print(mcsm_data['ligand_affinity_change'])
|
||||
# Extracting the categorical part (Destabillizing and Stabilizing) using word boundary ('ing')
|
||||
#aff_regex = re.compile(r'\b(\w+ing)\b')
|
||||
mcsm_data['ligand_outcome']= mcsm_data['PredAffLog'].str.extract(r'(\b\w+ing\b)', expand = True)
|
||||
print(mcsm_data['ligand_outcome'])
|
||||
print(mcsm_data['ligand_outcome'].value_counts())
|
||||
|
||||
# ensuring spellings are consistent
|
||||
american_spl = mcsm_data['ligand_outcome'].value_counts()
|
||||
print('Changing to Bristish spellings for col: ligand_outcome')
|
||||
mcsm_data['ligand_outcome'].replace({'Destabilizing': 'Destabilising', 'Stabilizing': 'Stabilising'}, inplace = True)
|
||||
print(mcsm_data['ligand_outcome'].value_counts())
|
||||
british_spl = mcsm_data['ligand_outcome'].value_counts()
|
||||
# compare series values since index will differ from spelling change
|
||||
check = american_spl.values == british_spl.values
|
||||
if check.all():
|
||||
print('PASS: spelling change successfull'
|
||||
, '\nNo. of predicted affinity changes:\n', british_spl
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: spelling change unsucessfull'
|
||||
, '\nExpected:\n', american_spl
|
||||
, '\nGot:\n', british_spl
|
||||
, '\n===============================================================')
|
||||
#%%===========================================================================
|
||||
# check dtype in cols: ensure correct dtypes for cols
|
||||
print('Checking dtypes in all columns:\n', mcsm_data.dtypes
|
||||
, '\n===================================================================')
|
||||
#1) numeric cols
|
||||
print('Converting the following cols to numeric:'
|
||||
, '\nligand_distance'
|
||||
, '\nduet_stability_change'
|
||||
, '\nligand_affinity_change'
|
||||
, '\n===================================================================')
|
||||
# using apply method to change stabilty and affinity values to numeric
|
||||
numeric_cols = ['duet_stability_change', 'ligand_affinity_change', 'ligand_distance']
|
||||
mcsm_data[numeric_cols] = mcsm_data[numeric_cols].apply(pd.to_numeric)
|
||||
|
||||
# check dtype in cols
|
||||
print('checking dtype after conversion')
|
||||
cols_check = mcsm_data.select_dtypes(include='float64').columns.isin(numeric_cols)
|
||||
if cols_check.all():
|
||||
print('PASS: dtypes for selected cols:', numeric_cols
|
||||
, '\nchanged to numeric'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL:dtype change to numeric for selected cols unsuccessful'
|
||||
, '\n===============================================================')
|
||||
#mcsm_data['ligand_distance', 'ligand_affinity_change'].apply(is_numeric_dtype(mcsm_data['ligand_distance', 'ligand_affinity_change']))
|
||||
print(mcsm_data.dtypes)
|
||||
#%%===========================================================================
|
||||
# Normalise values in DUET_change col b/w -1 and 1 so negative numbers
|
||||
# stay neg and pos numbers stay positive
|
||||
duet_min = mcsm_data['duet_stability_change'].min()
|
||||
duet_max = mcsm_data['duet_stability_change'].max()
|
||||
|
||||
duet_scale = lambda x : x/abs(duet_min) if x < 0 else (x/duet_max if x >= 0 else 'failed')
|
||||
|
||||
mcsm_data['duet_scaled'] = mcsm_data['duet_stability_change'].apply(duet_scale)
|
||||
print('Raw duet scores:\n', mcsm_data['duet_stability_change']
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\nScaled duet scores:\n', mcsm_data['duet_scaled'])
|
||||
#%%===========================================================================
|
||||
# Normalise values in affinity change col b/w -1 and 1 so negative numbers
|
||||
# stay neg and pos numbers stay positive
|
||||
aff_min = mcsm_data['ligand_affinity_change'].min()
|
||||
aff_max = mcsm_data['ligand_affinity_change'].max()
|
||||
|
||||
aff_scale = lambda x : x/abs(aff_min) if x < 0 else (x/aff_max if x >= 0 else 'failed')
|
||||
|
||||
mcsm_data['ligand_affinity_change']
|
||||
mcsm_data['affinity_scaled'] = mcsm_data['ligand_affinity_change'].apply(aff_scale)
|
||||
mcsm_data['affinity_scaled']
|
||||
print('Raw affinity scores:\n', mcsm_data['ligand_affinity_change']
|
||||
, '\n---------------------------------------------------------------'
|
||||
, '\nScaled affinity scores:\n', mcsm_data['affinity_scaled'])
|
||||
#=============================================================================
|
||||
# Adding colname: wild_pos: sometimes useful for plotting and db
|
||||
print('Creating column: wild_position')
|
||||
mcsm_data['wild_position'] = mcsm_data['wild_type'] + mcsm_data['position'].astype(str)
|
||||
print(mcsm_data['wild_position'].head())
|
||||
# Remove spaces b/w pasted columns
|
||||
print('removing white space within column: wild_position')
|
||||
mcsm_data['wild_position'] = mcsm_data['wild_position'].str.replace(' ', '')
|
||||
print('Correctly formatted column: wild_position\n', mcsm_data['wild_position'].head()
|
||||
, '\n===================================================================')
|
||||
#=============================================================================
|
||||
#%% ensuring dtypes are string for the non-numeric cols
|
||||
#) char cols
|
||||
char_cols = ['PredAffLog', 'mutation_information', 'wild_type', 'mutant_type', 'chain'
|
||||
, 'ligand_id', 'duet_outcome', 'ligand_outcome', 'wild_position']
|
||||
|
||||
#mcsm_data[char_cols] = mcsm_data[char_cols].astype(str)
|
||||
cols_check_char = mcsm_data.select_dtypes(include='object').columns.isin(char_cols)
|
||||
|
||||
if cols_check_char.all():
|
||||
print('PASS: dtypes for char cols:', char_cols, 'are indeed string'
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL:dtype change to numeric for selected cols unsuccessful'
|
||||
, '\n===============================================================')
|
||||
#mcsm_data['ligand_distance', 'ligand_affinity_change'].apply(is_numeric_dtype(mcsm_data['ligand_distance', 'ligand_affinity_change']))
|
||||
print(mcsm_data.dtypes)
|
||||
#%%
|
||||
#=============================================================================
|
||||
# Removing PredAff log column as it is not needed?
|
||||
print('Removing col: PredAffLog since relevant info has been extracted from it')
|
||||
mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
|
||||
#%%===========================================================================
|
||||
expected_ncols_toadd = 5 # beware of hardcoded numbers
|
||||
dforig_len = dforig_shape[1]
|
||||
expected_cols = dforig_len + expected_ncols_toadd
|
||||
if len(mcsm_dataf.columns) == expected_cols:
|
||||
print('PASS: formatting successful'
|
||||
, '\nformatted df has expected no. of cols:', expected_cols
|
||||
, '\ncolnames:', mcsm_dataf.columns
|
||||
, '\n----------------------------------------------------------------'
|
||||
, '\ndtypes in cols:', mcsm_dataf.dtypes
|
||||
, '\n----------------------------------------------------------------'
|
||||
, '\norig data shape:', dforig_shape
|
||||
, '\nformatted df shape:', mcsm_dataf.shape
|
||||
, '\n===============================================================')
|
||||
else:
|
||||
print('FAIL: something went wrong in formatting df'
|
||||
, '\nLen of orig df:', dforig_len
|
||||
, '\nExpected number of cols to add:', expected_ncols_toadd
|
||||
, '\nExpected no. of cols:', expected_cols, '(', dforig_len, '+', expected_ncols_toadd, ')'
|
||||
, '\nGot no. of cols:', len(mcsm_dataf.columns)
|
||||
, '\nCheck formatting:'
|
||||
, '\ncheck hardcoded value:', expected_ncols_toadd
|
||||
, '\nis', expected_ncols_toadd, 'the no. of expected cols to add?'
|
||||
, '\n===============================================================')
|
||||
#%%============================================================================
|
||||
# writing file
|
||||
print('Writing formatted df to csv')
|
||||
mcsm_dataf.to_csv(outfile, index = False)
|
||||
|
||||
print('Finished writing file:'
|
||||
, '\nFile:', outfile
|
||||
, '\nExpected no. of rows:', len(mcsm_dataf)
|
||||
, '\nExpected no. of cols:', len(mcsm_dataf.columns)
|
||||
, '\n=============================================================')
|
||||
#%%
|
||||
#End of script
|
||||
149
mcsm/ind_scripts/mcsm_results.py
Executable file
149
mcsm/ind_scripts/mcsm_results.py
Executable file
|
|
@ -0,0 +1,149 @@
|
|||
#!/usr/bin/env python3
|
||||
#=======================================================================
|
||||
#TASK:
|
||||
#=======================================================================
|
||||
#%% load packages
|
||||
import os,sys
|
||||
import subprocess
|
||||
import argparse
|
||||
import requests
|
||||
import re
|
||||
import time
|
||||
import pandas as pd
|
||||
from bs4 import BeautifulSoup
|
||||
#import beautifulsoup4
|
||||
from csv import reader
|
||||
#=======================================================================
|
||||
#%% specify input and curr dir
|
||||
homedir = os.path.expanduser('~')
|
||||
# set working dir
|
||||
os.getcwd()
|
||||
os.chdir(homedir + '/git/LSHTM_analysis/mcsm')
|
||||
os.getcwd()
|
||||
#=======================================================================
|
||||
#%% variable assignment: input and output
|
||||
#drug = 'pyrazinamide'
|
||||
#gene = 'pncA'
|
||||
|
||||
#drug = 'isoniazid'
|
||||
#gene = 'KatG'
|
||||
|
||||
drug = 'cycloserine'
|
||||
gene = 'alr'
|
||||
|
||||
#drug = args.drug
|
||||
#gene = args.gene
|
||||
|
||||
gene_match = gene + '_p.'
|
||||
#==========
|
||||
# data dir
|
||||
#==========
|
||||
datadir = homedir + '/' + 'git/Data'
|
||||
|
||||
#=======
|
||||
# input:
|
||||
#=======
|
||||
# 1) result_urls (from outdir)
|
||||
outdir = datadir + '/' + drug + '/' + 'output'
|
||||
in_filename_url = gene.lower() + '_result_urls.txt' #(outfile, sub write_result_url)
|
||||
infile_url = outdir + '/' + in_filename_url
|
||||
print('Input filename:', in_filename_url
|
||||
, '\nInput path(from output dir):', outdir
|
||||
, '\n=============================================================')
|
||||
|
||||
#=======
|
||||
# output
|
||||
#=======
|
||||
outdir = datadir + '/' + drug + '/' + 'output'
|
||||
out_filename = gene.lower() + '_mcsm_output.csv'
|
||||
outfile = outdir + '/' + out_filename
|
||||
print('Output filename:', out_filename
|
||||
, '\nOutput path:', outdir
|
||||
, '\n=============================================================')
|
||||
#=======================================================================
|
||||
def scrape_results(out_result_url):
|
||||
"""
|
||||
Extract results data using the result url
|
||||
|
||||
@params out_result_url: txt file containing result url
|
||||
one per line for each mutation
|
||||
@type string
|
||||
|
||||
returns: mcsm prediction results (raw)
|
||||
@type chr
|
||||
"""
|
||||
result_response = requests.get(out_result_url)
|
||||
# if results_response is not None:
|
||||
# page = results_page.text
|
||||
if result_response.status_code == 200:
|
||||
print('SUCCESS: Fetching results')
|
||||
else:
|
||||
print('FAIL: Could not fetch results'
|
||||
, '\nCheck if url is valid')
|
||||
# extract results using the html parser
|
||||
soup = BeautifulSoup(result_response.text, features = 'html.parser')
|
||||
# print(soup)
|
||||
web_result_raw = soup.find(class_ = 'span4').get_text()
|
||||
|
||||
return web_result_raw
|
||||
|
||||
|
||||
def build_result_dict(web_result_raw):
|
||||
"""
|
||||
Build dict of mcsm output for a single mutation
|
||||
Format web results which is preformatted to enable building result dict
|
||||
# preformatted string object: Problematic!
|
||||
# make format consistent
|
||||
|
||||
@params web_result_raw: directly from html parser extraction
|
||||
@type string
|
||||
|
||||
@returns result dict
|
||||
@type {}
|
||||
"""
|
||||
|
||||
# remove blank lines from web_result_raw
|
||||
mytext = os.linesep.join([s for s in web_result_raw.splitlines() if s])
|
||||
|
||||
# affinity change and DUET stability change cols are are split over
|
||||
# multiple lines and Mutation information is empty!
|
||||
mytext = mytext.replace('ange:\n', 'ange: ')
|
||||
#print(mytext)
|
||||
|
||||
# initiliase result_dict
|
||||
result_dict = {}
|
||||
for line in mytext.split('\n'):
|
||||
fields = line.split(':')
|
||||
# print(fields)
|
||||
if len(fields) > 1: # since Mutaton information is empty
|
||||
dict_entry = dict([(x, y) for x, y in zip(fields[::2], fields[1::2])])
|
||||
result_dict.update(dict_entry)
|
||||
|
||||
return result_dict
|
||||
#=====================================================================
|
||||
#%% call function
|
||||
#request_results(infile_url)
|
||||
#response = requests.get('http://biosig.unimelb.edu.au/mcsm_lig/results_prediction/1586364780.41')
|
||||
results_interim = scrape_results('http://biosig.unimelb.edu.au/mcsm_lig/results_prediction/1587053996.55')
|
||||
result_dict = build_result_dict(results_interim)
|
||||
|
||||
output_df = pd.DataFrame()
|
||||
|
||||
url_counter = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
|
||||
infile_len = os.popen('wc -l < %s' % infile_url).read() # quicker than using Python :-)
|
||||
print('Total URLs:',infile_len)
|
||||
|
||||
with open(infile_url, 'r') as urlfile:
|
||||
for line in urlfile:
|
||||
url_line = line.strip()
|
||||
# response = request_results(url_line)
|
||||
#response = requests.get(url_line)
|
||||
results_interim = scrape_results(url_line)
|
||||
result_dict = build_result_dict(results_interim)
|
||||
print('Processing URL: %s of %s' % (url_counter, infile_len))
|
||||
df = pd.DataFrame(result_dict, index=[url_counter])
|
||||
url_counter += 1
|
||||
output_df = output_df.append(df)
|
||||
|
||||
#print(output_df)
|
||||
output_df.to_csv(outfile, index = None, header = True)
|
||||
240
mcsm/ind_scripts/run_mcsm.py
Executable file
240
mcsm/ind_scripts/run_mcsm.py
Executable file
|
|
@ -0,0 +1,240 @@
|
|||
#!/usr/bin/env python3
|
||||
#=======================================================================
|
||||
#TASK:
|
||||
#=======================================================================
|
||||
#%% load packages
|
||||
import os,sys
|
||||
import subprocess
|
||||
import argparse
|
||||
import requests
|
||||
import re
|
||||
import time
|
||||
import pandas as pd
|
||||
from bs4 import BeautifulSoup
|
||||
#from csv import reader
|
||||
#=======================================================================
|
||||
#%% specify input and curr dir
|
||||
homedir = os.path.expanduser('~')
|
||||
# set working dir
|
||||
os.getcwd()
|
||||
os.chdir(homedir + '/git/LSHTM_analysis/mcsm')
|
||||
os.getcwd()
|
||||
#=======================================================================
|
||||
#%% command line args
|
||||
#arg_parser = argparse.ArgumentParser()
|
||||
#arg_parser.add_argument('-d', '--drug', help='drug name', default = 'pyrazinamide')
|
||||
#arg_parser.add_argument('-g', '--gene', help='gene name', default = 'pncA') # case sensitive
|
||||
#arg_parser.add_argument('-d', '--drug', help='drug name', default = 'TESTDRUG')
|
||||
#arg_parser.add_argument('-g', '--gene', help='gene name (case sensitive)', default = 'testGene') # case sensitive
|
||||
#args = arg_parser.parse_args()
|
||||
#=======================================================================
|
||||
#%% variable assignment: input and output
|
||||
#drug = 'pyrazinamide'
|
||||
#gene = 'pncA'
|
||||
|
||||
#drug = 'isoniazid'
|
||||
#gene = 'KatG'
|
||||
|
||||
drug = 'cycloserine'
|
||||
gene = 'alr'
|
||||
|
||||
|
||||
#drug = args.drug
|
||||
#gene = args.gene
|
||||
|
||||
gene_match = gene + '_p.'
|
||||
#==========
|
||||
# data dir
|
||||
#==========
|
||||
datadir = homedir + '/' + 'git/Data'
|
||||
|
||||
#==========
|
||||
# input dir
|
||||
#==========
|
||||
indir = datadir + '/' + drug + '/' + 'input'
|
||||
|
||||
#==========
|
||||
# output dir
|
||||
#==========
|
||||
outdir = datadir + '/' + drug + '/' + 'output'
|
||||
|
||||
#=======
|
||||
# input files:
|
||||
#=======
|
||||
# 1) pdb file
|
||||
in_filename_pdb = gene.lower() + '_complex.pdb'
|
||||
infile_pdb = indir + '/' + in_filename_pdb
|
||||
print('Input pdb file:', infile_pdb
|
||||
, '\n=============================================================')
|
||||
|
||||
# 2) mcsm snps
|
||||
in_filename_snps = gene.lower() + '_mcsm_snps.csv' #(outfile2, from data_extraction.py)
|
||||
infile_snps = outdir + '/' + in_filename_snps
|
||||
print('Input mutation file:', infile_snps
|
||||
, '\n=============================================================')
|
||||
|
||||
#=======
|
||||
# output files
|
||||
#=======
|
||||
|
||||
# 1) result urls file
|
||||
#result_urls_filename = gene.lower() + '_result_urls.txt'
|
||||
#result_urls = outdir + '/' + result_urls_filename
|
||||
|
||||
# 2) invalid mutations file
|
||||
#invalid_muts_filename = gene.lower() + '_invalid_mutations.txt'
|
||||
#outfile_invalid_muts = outdir + '/' + invalid_muts_filename
|
||||
|
||||
#print('Result url file:', result_urls
|
||||
# , '\n==================================================================='
|
||||
# , '\nOutput invalid muations file:', outfile_invalid_muts
|
||||
# , '\n===================================================================')
|
||||
|
||||
#%% global variables
|
||||
host = "http://biosig.unimelb.edu.au"
|
||||
prediction_url = f"{host}/mcsm_lig/prediction"
|
||||
#=======================================================================
|
||||
def format_data(data_file):
|
||||
"""
|
||||
Read file containing SNPs for mcsm analysis and remove duplicates
|
||||
|
||||
@param data_file csv file containing nsSNPs for given drug and gene.
|
||||
csv file format:
|
||||
single column with no headers with nsSNP format as below:
|
||||
A1B
|
||||
B2C
|
||||
@type data_file: string
|
||||
|
||||
@return unique SNPs
|
||||
@type list
|
||||
"""
|
||||
data = pd.read_csv(data_file, header = None, index_col = False)
|
||||
data = data.drop_duplicates()
|
||||
mutation_list = data[0].tolist()
|
||||
# print(data.head())
|
||||
return mutation_list
|
||||
|
||||
def request_calculation(pdb_file, mutation, chain, ligand_id, wt_affinity, prediction_url, output_dir, gene_name):
|
||||
"""
|
||||
Makes a POST request for a ligand affinity prediction.
|
||||
|
||||
@param pdb_file: valid path to pdb structure
|
||||
@type string
|
||||
|
||||
@param mutation: single mutation of the format: {WT}<POS>{Mut}
|
||||
@type string
|
||||
|
||||
@param chain: single-letter(caps)
|
||||
@type chr
|
||||
|
||||
@param lig_id: 3-letter code (should match pdb file)
|
||||
@type string
|
||||
|
||||
@param wt affinity: in nM
|
||||
@type number
|
||||
|
||||
@param prediction_url: mcsm url for prediction
|
||||
@type string
|
||||
|
||||
@return response object
|
||||
@type object
|
||||
"""
|
||||
with open(pdb_file, "rb") as pdb_file:
|
||||
files = {"wild": pdb_file}
|
||||
body = {
|
||||
"mutation": mutation,
|
||||
"chain": chain,
|
||||
"lig_id": ligand_id,
|
||||
"affin_wt": wt_affinity
|
||||
}
|
||||
|
||||
response = requests.post(prediction_url, files = files, data = body)
|
||||
# print(response.status_code)
|
||||
# result_status = response.raise_for_status()
|
||||
if response.history:
|
||||
# if result_status is not None: # doesn't work!
|
||||
print('PASS: valid mutation submitted. Fetching result url')
|
||||
# response = requests.post(prediction_url, files = files, data = body)
|
||||
# return response
|
||||
url_match = re.search('/mcsm_lig/results_prediction/.+(?=")', response.text)
|
||||
url = host + url_match.group()
|
||||
#===============
|
||||
# writing file: result urls
|
||||
#===============
|
||||
out_url_file = output_dir + '/' + gene_name.lower() + '_result_urls.txt'
|
||||
myfile = open(out_url_file, 'a')
|
||||
myfile.write(url + '\n')
|
||||
myfile.close()
|
||||
|
||||
else:
|
||||
print('ERROR: invalid mutation! Wild-type residue doesn\'t match pdb file.'
|
||||
, '\nSkipping to the next mutation in file...')
|
||||
#===============
|
||||
# writing file: invalid mutations
|
||||
#===============
|
||||
out_error_file = output_dir + '/' + gene_name.lower() + '_errors.txt'
|
||||
failed_muts = open(out_error_file, 'a')
|
||||
failed_muts.write(mutation + '\n')
|
||||
failed_muts.close()
|
||||
|
||||
#def write_result_url(holding_page, out_result_url, host):
|
||||
# """
|
||||
# Extract and write results url from the holding page returned after
|
||||
# requesting a calculation.
|
||||
|
||||
# @param holding_page: response object containinig html content
|
||||
# @type object
|
||||
|
||||
# @param out_result_url: txt file containing urls for mcsm results
|
||||
# @type string
|
||||
|
||||
# @param host: mcsm server name
|
||||
# @type string
|
||||
|
||||
# @return None, writes a file containing result urls (= total no. of muts)
|
||||
# """
|
||||
# if holding_page:
|
||||
# url_match = re.search('/mcsm_lig/results_prediction/.+(?=")', holding_page.text)
|
||||
# url = host + url_match.group()
|
||||
#===============
|
||||
# writing file
|
||||
#===============
|
||||
# myfile = open(out_result_url, 'a')
|
||||
# myfile.write(url+'\n')
|
||||
# myfile.close()
|
||||
# print(myfile)
|
||||
# return url
|
||||
#%%
|
||||
#=======================================================================
|
||||
# variables to run mcsm lig predictions
|
||||
#pdb_file = infile_snps_pdb
|
||||
my_chain = 'A'
|
||||
my_ligand_id = 'DCS'
|
||||
my_affinity = 10
|
||||
|
||||
print('Result urls and error file (if any) will be written in: ', outdir)
|
||||
|
||||
# call function to format data to remove duplicate snps before submitting job
|
||||
mcsm_muts = format_data(infile_snps)
|
||||
mut_count = 1 # HURR DURR COUNT STARTEDS AT ONE1`!1
|
||||
infile_snps_len = os.popen('wc -l < %s' % infile_snps).read() # quicker than using Python :-)
|
||||
print('Total SNPs for', gene, ':', infile_snps_len)
|
||||
for mcsm_mut in mcsm_muts:
|
||||
print('Processing mutation: %s of %s' % (mut_count, infile_snps_len), mcsm_mut)
|
||||
print('Parameters for mcsm_lig:', in_filename_pdb, mcsm_mut, my_chain, my_ligand_id, my_affinity, prediction_url, outdir, gene)
|
||||
# function call: to request mcsm prediction
|
||||
# which writes file containing url for valid submissions and invalid muts to respective files
|
||||
holding_page = request_calculation(infile_pdb, mcsm_mut, my_chain, my_ligand_id, my_affinity, prediction_url, outdir, gene)
|
||||
# holding_page = request_calculation(infile_pdb, mcsm_mut, my_chain, my_ligand_id, my_affinity, prediction_url, outdir, gene)
|
||||
time.sleep(1)
|
||||
mut_count += 1
|
||||
# result_url = write_result_url(holding_page, result_urls, host)
|
||||
|
||||
print('Request submitted'
|
||||
, '\nCAUTION: Processing will take at least ten'
|
||||
, 'minutes, but will be longer for more mutations.')
|
||||
|
||||
#%%
|
||||
|
||||
|
||||
|
||||
Loading…
Add table
Add a link
Reference in a new issue